Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques
Abstract
Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and there is a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives of this study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives of this study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences.
Keywords
References
Mukhamediev, R. I.; Popova, Y.; Kuchin, Y.; Zaitseva, E.; Kalimoldayev, A.; Symagulov, A.; Levashenko, V.; Abdoldina, F.; Gopejenko, V.; Yakunin, K.; Muhamedijeva, E., and Yelis, M. 2022. Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges. Maths. 10(15) 1-25. DOI: 10.3390/math10152552.
Verdu, E.; Nieto, Y. V., and Saleem, N. 2023. Big data and artificial intelligence in earth science: recent progress and future advancements. A Geo. 71(3) 1373-1375. DOI: 10.1007/s11600-023-01051-2.
Chang, Z.; Catani, F.; Huang, F.; Liu, G.; Meena, S. R.; Huang, J., and Zhou, C. 2023. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors. J R Mechs and Geo Eng. 15(5) 1127-1143. DOI: 10.1016/j.jrmge.2022.07.009.
Lawal, A. I. and Kwon, S. 2021. Application of artificial intelligence to rock mechanics: An overview. J R Mechs Geo Eng. 13(1) 248-266. DOI: 10.1016/j.jrmge.2020.05.010.
Maxwell, A. E.; Sharma, M.; Kite, J. S.; Donaldson, K. A.; Thompson, J. A.; Bell, M. L., and Maynard, S. M. 2020. Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt. Rem Sens. 12(3) 1-27. DOI: 0.3390/rs12030486.
Ahmad, A. B. and Tsuji, T. 2021. Machine learning for automatic slump identification from 3D seismic data at convergent plate margins. Mar Pet Geo. 133 (2021) 1-13. DOI: 10.1016/j.marpetgeo.2021.105290.
Zheng, Y.; Zhang, Q.; Yusifov, A., and Shi, Y. 2019. Applications of supervised deep learning for seismic interpretation and inversion. Lead E. 38 (7) 526-533. DOI: 0.1190/tle38070526.1.
Amer, M. and Al-Obaidi, D. 2024. Permeability prediction and facies distribution for yamama reservoir in Faihaa oil field: Role of machine learning and cluster analysis approach. I Geo Jo. 57(1C) 29-46. DOI: 10.46717/igj.57.1C.3ms-2024-3-15.
Hoveyzavi, R.; Nabi-Bidhendi, M.; Kadkhodaie, A.; Nakhaee, A., and Parchekhari, S. 2022. Reservoir quality evaluation based on integration of artificial intelligence and NMR-derived Electrofacies. J Pet Sci and Tech. 12(1) 62-74. DOI: 10.22078/jpst.2022.4651.1766.
Lichun, K.; He, L.; Yili, R.; Kai, L.; Mingyu, S.; Jian, S., and Xin, L. 2021. Application and development trend of artificial intelligence in petroleum exploration and development. Pet expl dev. 48(1) 1-14. DOI: 10.1016/S1876-3804(21)60001-0.
Sircar, A.; Yadav, K.; Rayavarapu, K.; Bist, N., and Oza, H. 2021. Application of machine learning and artificial intelligence in oil and gas industry. Pet Res. 6(4) 379-391. DOI: 10.1016/j.ptlrs.2021.05.009.
Solanki, P.; Baldaniya, D.; Jogani, D.; Chaudhary, B.; Shah, M., and Kshirsagar, A. 2022. Artificial intelligence: New age of transformation in petroleum upstream. Pet Res. 7(1) 106-114. DOI: 10.1016/j.ptlrs.2021.07.002.
Jiao, P. and Alavi, A. H. 2020. Artificial intelligence in seismology: Advent, performance and future trends. Geo Fronts. 11(3) 739-744. DOI: 10.1016/j.gsf.2019.10.004.
Kong, Q.; Daniel T. Trugman; Zachary E. Ross; Michael J. Bianco; Brendan J. Meade, and Gerstoft, P. 2018. Machine learning in seismology: Turning data into insights. Seis Res Lets. 90(1) 3-14. https://doi.org/10.1785/0220180259.
Lin, N.; Chen, Y.; Liu, H., and Liu, H. 2021. A comparative study of machine learning models with hyperparameter optimization algorithm for mapping mineral prospectivity. Mins. 11(2) 1-28. DOI: 10.3390/min11020159.
Sun, T.; Li, H.; Wu, K.; Chen, F.; Zhu, Z., and Hu, Z. 2020. Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from Southern Jiangxi Province, China. Mins. 10(2) 2-28. DOI: 10.3390/min10020102.
Sun, T.; Chen, F.; Zhong, L.; Liu, W., and Wang, Y. 2019. GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geo Rev. 109 (6) 26-49. DOI: 10.1016/j.oregeorev.2019.04.003.
Serra, O. and Serra, L. 2003 Well Logging and Geology (Paris: Technip).
Schlumberger 2009 Log interpretation charts (USA: Schlumberger).
Schlumberger 1989 Log interpretation principles/ applications (USA: Schlumberger ).
Puskarczyk, E. 2019. Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations. A Geo. 67(6), 1991-2003. DOI: 10.1007/s11600-019-00359-2.
Shao, R.; Wang, H., and Xiao, L. 2024. Reservoir evaluation using petrophysics informed machine learning: A case study. AI in Geo. 5(12) 1-18. DOI: 10.1016/j.aiig.2024.100070.
Adhari, M. R. and Kardawi, M. Y. 2022. Estimation of density log and sonic log using artificial intelligence: an example from the Perth Basin, Australia. J Geo, Eng, Env, and Tech. 7(4) 158-166. DOI: 10.25299/jgeet.2022.7.4.10050.
Bhattacharya, S. and Mishra, S. 2018. Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA. J Petr Sci and Eng. 170 1005-1017. DOI: 10.1016/j.petrol.2018.06.075.
Gorter, J. D. and Davies, J. M. 1999. Upper Permian carbonate reservoirs of the North West Shelf and northern Perth Basin, Australia APPEA J. 39(1,)343-363.
Hall, P. B. and Kneale, R. L. 1992. Perth Basin Rejuvenated. APPEA J. 32(1) 33-43.
Owad-Jones, D. and Ellis, G. 2000 Western Australia atlas of petroleum fields: Onshore Perth Basin, volume 1, (Australia: Department of Minerals and Energy).
RISC; HELIX, and ISIS 2000 Review of the Woodada gas field, Perth, Australia. (Australia: Helix).
Cadman, S. J.; Pain, L., and Vuckovic, V. 1994 Perth Basin, Western Australia, Australian Petroleum Accumulation Report 10. (Australia: Bureau of Resources Sciences).
Crostella, A. and Backhouse, J. 2000 Geology and petroleum exploration of the central and southern Perth Basin, Report 57. (Australia: Department of Minerals and Energy).
Mory, A. J. and Iasky, R. P. 1996 Stratigraphy and structure of the onshore northern Perth Basin, Western Australia, Report 46 (Australia: Department of Minerals and Energy).
Iasky, R. P. and Mory, A. J. 1993. Structural and Tectonic Framework of the Onshore Northern Perth Basin. Exploration Geophysics. 24(4),585-592.
Crostella, A. 1995 An evaluation of the hydrocarbon potential of the onshore northern Perth Basin, Western Australia, Report 43 (Australia: Department of Minerals and Energy).
D'Ercole, C.; Pitchford, A., and Mory, A. J. 2003 Leads and prospects within tenements of the northern Perth Basin, Western Australia, 2002, Record 2003/4. (Australia: Geological Survey of Western Australia).
Harris, L. B. 1994. Structural and tectonic synthesis for the Perth Basin, Western Australia. J Pet Geo. 17(2) 129-156.
Kovac, P.; Cevallos, C., and Feijth, J. 2016. Targeting oil and gas in the Perth Basin using an airborne gravity gradiometer. First break. 34(4) 51-58.
Thomas, C. M. 2014 The tectonic framework of the Perth Basin: Current understanding, Record 2014/14. (Australia: Geological Survey of Western Australia).
Song, T. and Cawood, P. A. 2000. Structural styles in the Perth Basin associated with the Mesozoic break-up of Greater India and Australia. Tectonophysics. 317(1) 55-72.
Song, T. and Cawood, P. A. 1999. Multistage deformation of linked fault systems in extensional regions: an example from the northern Perth Basin, Western Australia. Aust J of Earth Sci. 46 (6) 897-903.
Arcbold, N. W. and Shi, G. R. 1995. Permian Brachiopod faunas of Western Australia: Gondwanan-Asian relationship and Permian climate. J S Asian Earth Sci. 11(3) 207-215.
Backhouse, J. 1994 Palynological reassessment of selected Permian/ Triassic intervals from Beharra Springs 2 and 3, and Mountain Bridge 1 (Australia: Western Australia Geological Survey).
Adhari, M. R.; Marwan, M.; Husin, H.; Ismail, N., and Ghani, A. A. 2024. Understanding Heterogeneities in a Mixed Carbonate-Siliciclastic Reservoir of the Perth Basin, Australia. I Geo J. 57 (2F) 122-149. DOI: 10.46717/igj.57.2F.9ms-2024-12-19.
Adhari, M. R.; Marwan, M.; Husin, H.; Ismail, N., and Ghani, A. A. 2025. The Importance of Fracture Systems as Hydrocarbon Migration Pathways in a Mixed Carbonate–Siliciclastic Reservoir of Western Australia. IOP Conf Series: Earth and Env Sci. 1521 1-10. DOI: 10.1088/1755-1315/1521/1/012001.
Adhari, M. R.; Marwan; Husin, H.; Ismail, N., and Ghani, A. A. 2025. Spatio-temporal characterisation of the Beekeeper Formation of Australia: facies development, mixing of carbonate and siliciclastic materials, and influencing factors. Pet Res. 10(3) 443-457. DOI: 10.1016/j.ptlrs.2025.03.001.
Lane, C. M. and Watson, R. J. 1985. Development of the Woodada gas field. APPEA J. 25(1) 316-328.
Bai, L.; Liang, J., and Cao, F. 2020. A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters. Info Fus. 61(11) 36-47. DOI: 10.1016/j.inffus.2020.03.009.
Kaufman, L. and Rousseeuw, P. J. 1990 Finding groups in data (New York, USA: Wiley).
Aggarwal, C. C. 2018 Neural networks and deep learning, a textbook (New York, USA: Springer).
Khazaie, E.; Noorian, Y.; Moussavi-Harami, R.; Mahboubi, A.; Kadkhodaie, A., and Omidpour, A. 2022. Electrofacies modeling as a powerful tool for evaluation of heterogeneities in carbonate reservoirs: A case from the Oligo-Miocene Asmari Formation (Dezful Embayment, southwest of Iran). J Afr Earth Sci. 195. DOI: 10.1016/j.jafrearsci.2022.104676.
Mahadasu, P. and Singh, K. H. 2022. Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models. Arab J Sci and Eng. 48(6) 7855-7866. DOI: 0.1007/s13369-022-07521-x.
Alameedy, U.; Almomen, A., and Abd, N. 2023. Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data. I Geo J. 56(1D) 175-187. DOI: 10.46717/igj.56.1D.14ms-2023-4-23.
Eftekhari, S. H.; Memariani, M.; Maleki, Z.; Aleali, M., and Kianoush, P. 2024. Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods. Sci Rep. 14 (1) 1-22. DOI: 10.1038/s41598-024-55955-0.
Geoscience-Australia 2020 Regional Geology of the northern Perth Basin (Australia: Geoscience Australia).
Chiarella, D.; Longhitano, S. G., and Tropeano, M. 2017. Types of mixing and heterogeneities in siliciclastic-carbonate sediments. Mar Pet Geo. 88 (12), 617-627. DOI: 10.1016/j.marpetgeo.2017.09.010.
Rieux, A.; Weill, P.; Mouazé, D., and Tessier, B. 2023. Influence of sediment composition on morphology and internal structure of mixed siliciclastic–bioclastic coastal barriers: Contribution of flume experiments. Sed. 70(5) 1580-1600. DOI: 10.1111/sed.13083.
Carrasquilla, A. and Rocha, H. 2025. Enhanced clustering techniques for lithofacies and electrofacies identification in post-salt carbonate and siliciclastic reservoirs: a case study from the campos basin, Brazil. Unconv Res. 8(1) 1-17. DOI: 10.1016/j.uncres.2025.100211.
Abbas, M. A.; Al-Mudhafar, W. J.; Anees, A., and Wood, D. A. 2024. Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs. Enr Geo. 5(4) 1-15. DOI:10.1016/j.engeos.2024.100341
Khalid, M. S.; Mansour, A. S.; Desouky, S. E. D. M.; Afify, W. S. M.; Ahmed, S. F., and Elnaggar, O. M. 2024. Carbonate reservoir characterization and permeability modeling using Machine Learning ـــ a study from Ras Fanar field, Gulf of Suez, Egypt. Earth Sci Info. 17(7) 4675–4694. DOI: 10.1007/s12145-024-01406-3.
DOI: 10.24815/jn.v25i3.47470
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